Antenna system for breast cancer detection

The innovative knowledgebase system enables the healthcare system to address patient’s customized requirements. Developing and implementing this system with context-aware modifications is a challenge for developing countries, where it can change the healthcare delivery landscape from diagnosis to treatment. Although many studies have been conducted on the development of a knowledge-based system for early breast cancer detection using a rule-based approach, they lack contextual factors such as cultural aspects and socioeconomic features. This paper proposes a contextually aware machine learning (ML)-based knowledge base system for early breast cancer detection in the Pakistani context. Established risk and preventive breast cancer factors were identified from an extensive literature review and validated by academic and industry experts. Contextual factors were investigated with the assistance of field experts, and predictions from the ML algorithm were duly validated. The nested if-then rule, based on established risk and preventive factors, was combined with ML prediction to form the foundation of the proposed knowledgebase system. This research will improve the performance of healthcare systems in terms of accuracy and cost-effectiveness.

Breast cancer is the most common form of cancer in women. Its aggressive nature has made it one of the chief factors of high female mortality. Therefore, this has motivated research to achieve early diagnosis since it is the best strategy for patient survival. Currently, mammography is the gold standard for detecting breast cancer. However, it is expensive, unsuitable for dense breasts, and an invasive process that exposes the patient to radiation. Infrared thermography is gaining popularity as a screening modality for the early detection of breast cancer. It is a noninvasive and cost-effective modality that allows health practitioners to observe the temperature profile of the breast region for signs of cancerous tumors. Deep learning has emerged as a powerful computational tool for the early detection of breast cancer in radiology. As such, this study presents a review that shows existing work on deep learning-based Computer-aided Diagnosis (CADx) systems for breast cancer detection. In the same context, it reflects on classification utilizing breast thermograms. It first provides an overview of infrared thermography, details on available breast thermogram datasets, and then segmentation techniques applied to these thermograms. We also provide a brief overview of deep neural networks. Finally, it reviews works adopting Deep Neural Networks (DNNs) for breast thermogram classification.

Most of the currently established techniques for breast cancer have inherent disadvantages, such as destructive effects, image accuracy, and others. In addition, mammography and nuclear methods have a potential for producing harmful ionization effects in humans. Another technique for breast cancer detection is microwave imaging radar, which has the advantage of a less hazardous modality and is a non-destructive method. However, the weakness of the received backscattered signal is a major concern for this technique. Furthermore, there is a limitation to the size of tumour that can be detected. Therefore, this study analyzes the feasibility of ultra wideband (UWB) and microwave forward scattering radar (FSR) system for breast cancer detection. Two parameters which are UWB FSR radar cross section (UWB FS RCS) and received Doppler frequency profile were investigated. The results had shown a significant difference in terms of RCS amplitude of more than 2 dBm2 between normal and cancerous breast which can be used as a clue to detect the presence of tumor. The simulation shows a cancerous breast having Doppler frequency higher than the Doppler value in normal breast. Key words: Early breast cancer detection, ultra wideband (UWB) radar imaging.

The most common type of cancer among women worldwide is breast cancer. Early detection of breast cancer is very important to reduce the fatality rate. For the hundreds of mammographic images scanned by a radiologist, only a few are cancerous. While detecting abnormalities, some of them may be missed, as the detection of suspicious and abnormal images is a recurrent mission that causes fatigue and eyestrain. In this paper, a computer-aided diagnosis system using the curvelet transform (CT) algorithm is proposed for interpreting mammograms to improve the decision making. The purpose of this study is to develop a method for the characterization of the mammography as both normal and abnormal regions, and to determine its diagnostic performance to differentiate between malignant and benign ones. The multiresolution CT that was recently derived is used to differentiate among 200 mammograms: 50 malignant, 50 benign, and 100 normal. A support vector machine and the k-nearest neighbor algorithm are used as classifiers to build the diagnostic model and are also used for the principal component analysis and linear discriminant analysis for further dimensional reduction and feature selection. A dataset from the Mammographic Image Analysis Society database is used for testing the method.

A time‐domain microwave imaging system with improved image quality is presented for early detection of breast cancer. The time‐domain system has the advantage of short scan time compared with the frequency‐domain system but suffers from low signal‐to‐noise ratio (SNR) due to high attenuation of the RF signal within the breast tissue. Signal averaging of repeated measurements can improve the SNR, but this method also smears the signal peaks due to the signal‐averaged jitter, which adversely affects the reconstructed image quality. An image enhancement method using Gaussian bandpass filtering (BPF) is proposed for a time‐domain imaging system. The results from the proposed method were compared with those from the deconvolution method, which is typically used to remove the effects of signal‐averaged timing jitter in short pulse measurements. Simulation results with a 3D hemispherical homogeneous breast model containing tumour cells revealed improvement of the reconstructed image quality with the proposed method in the presence of timing jitter. The signal‐to‐mean ratio was increased from 6.46 to 14.15 with BPF, whereas deconvolution resulted in an increase to 9.36. Simulation results with a non‐homogeneous realistic breast model also showed image enhancement with the proposed method.

Population-based screening programs for the early detection of breast cancer have significantly reduced mortality in women, but they are resource intensive in terms of time, cost, and workload and still have limitations mainly due to the use of 2D imaging techniques, which may cause overlapping of tissues, and interobserver variability. Artificial intelligence (AI) systems may be a valuable tool to assist radiologist when reading and classifying mammograms based on the malignancy of the detected lesions. However, there are several factors that can influence the outcome of a mammogram and thus also the detection capability of an AI system. The aim of our work is to analyze the robustness of the diagnostic ability of an AI system designed for breast cancer detection. Mammograms from a population-based screening program were scored with the AI system. The sensitivity and specificity by means of the area under the receiver operating characteristic (ROC) curve were obtained as a function of the mammography unit manufacturer, demographic characteristics, and several factors that may affect the image quality (age, breast thickness and density, compression applied, beam quality, and delivered dose). The area under the curve (AUC) from the scoring ROC curve was 0.92 (95% confidence interval = 0.89 - 0.95). It showed no dependence with any of the parameters considered, as the differences in the AUC for different interval values were not statistically significant. The results suggest that the AI system analyzed in our work has a robust diagnostic capability, and that its accuracy is independent of the studied parameters.

This work intends to an integration of implementing an automated diagnostic systems for breast cancer detection using Artificial Neural Network (ANN) in FPGA. In the world, breast cancer is the fifth most common cause of cancer death. So better classification system is needed for diagnosing breast cancer disease. In this work, the training and testing of the Multilayer Perceptron Neural Network (MLPNN) with Back Propagation Network (BPN) is done with the attributes of the record of the Wisconsin Breast Cancer Database (WBCD). The neural network lacks the flexibility during off line training. In order to overcome the flexibility, it is necessary to train and test the network on on-chip neural network using FPGA. The purpose is to determine the cancer of patients either having benign or malignant through an FPGA based implementation of smart instrument. In order to implement the hardware, VERILOG coding is done for ANN and synthesized by Xilinx family XC5VLX50TFFT1136 FPGA Virtex 5 board using XILINX ISE tool to get the netlist of ANN. Finally the netlist is mapped to FPGA and the hardware functionality is verified. The correct classification rate of proposed system is 90.83%.

A microwave system for breast cancer detection has been modeled numerically (see Fear, E.C. and Stuchly, M.A., IEEE Trans. Microwave Theory Tech., vol.48, p.1854-63, 2000). This system is based on the principle of confocal microwave imaging (CMI). In our system, a woman lies in the prone position with the breasts immersed in a liquid (low loss, dielectric constant similar to that of breast tissue). One or more antennas are mechanically scanned around the breast, forming a synthetic array. A short pulse containing a broad range of frequencies (5 GHz or more) is transmitted from each antenna position and the same antenna receives a scattered pulse. Numerical simulations of the system performance show that tumors of the order of 3 mm diameter can be detected and located. In the simulations, a contrast of about 5 in dielectric constant and 10 in loss factor is used for the normal and malignant breast tissues. In order to verify our cylindrical CMI system experimentally, a test system has been designed to image analogous contrasts in the dielectric constant of representative materials. Results of the experiment and associated signal processing are presented.

Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.

Breast cancer is the second most common type of cancer among women in the USA, and it is very common to appear in its invasive form. Detecting its presence in the early stages can potentially aid in the mortality rate depletion since at that point large tumours are highly unlikely to have developed. Technological advances of the last decades have provided advanced tools that employ machine learning for early detection. Common techniques include tumour imaging using special equipment that in most cases is not widely accessible. In order to overcome this limitation, new techniques that employ blood-based biomarkers are being explored. In the current work machine learning algorithms are exploited for the development of a decision support system for breast cancer using easily obtainable user information, age, body mass index, glucose and resistin. The explored algorithms include Logistic Regression, Naive Bayes, Support Vector Machine and Gradient Boosting Classification, all of which are used for the classification of new patients based on a dataset that includes information from previous breast cancer incidents. The results depict that the optimal algorithm based on the current methodology and implementation is the Gradient Boosting Classification which exhibits the highest prediction scores. In order to ensure wide accessibility, a mobile application is developed. The user can easily provide the required information for the prediction to the application and obtain the results rapidly.

We have previously reported investigations of a novel pulsed confocal microwave system for detection of breast tumors (Hagness et al., 1997). In this paper, we extend this work by identifying a frequency window for optimum operation of the tumor detection system. The finite difference time-domain (FDTD) method is used to study a simplified 2-D breast-tissue geometry adjacent to an elliptical reflector antenna. First, power density results are presented to demonstrate the focusing abilities of the reflector antenna for sinusoid excitation at 3, 6 and 9 GHz. Second, a tumor located at the in-breast focus of the antenna is included in the geometry in order to observe the pulse response of the detector as a function of the tumor size at center frequencies of 3, G and 9 GHz.

Thank you very much for joining EUSOBI 2021 ONLINE and celebrating the Breast Cancer Awareness Month with us! This year we had a terrific programme and we are indebted to Dr. Ritse Mann and his Programme Committee for all their hard work in putting this together. We had a wonderful selection of international speakers who gave the latest updates in all your favourite topics and ensured that you are kept up to date with the latest developments in breast imaging. A special focus was given to the interactivity of this year’s programme, by adding expert discussions and concluding with the first virtual edition of our famous Breast Interpretation Quiz. Thanks to all of you who submitted abstracts and therefore having been an important part of this scientific event. We are also very grateful for the support of our industry partners who are listed here: https://www.eusobi.org/congress-sponsors-2/ Most of them have contributed to the extension of our scientific programme by offering interesting sessions and workshops. Have a look by scrolling through all sessions. All sessions have been recorded and are available on-demand.

In screening X-ray mammography two different views are captured and analysed of both breasts. In the four X-ray images some special signs are looked for. First the individual images are analysed independently, but good result can be achieved only if joint analysis of the images is also done. This paper proposes a simple procedure for the joint analysis of the breast's two views. The procedure is based upon the experiences of radiologists: masses and calcifications should emerge on both views, so if no matching is found, the given object is a false positive hit. First a reference system is evolved to find corresponding regions on the two images. Calcification clusters obtained in individual images are matched in “2.5 D” provided by the reference system. Masses detected in individual images are further examined with texture segmentation. The proposed approach can significantly reduce the number of false positive hits both in calcification and in mass detection.

<i>Objective:</i> This paper assesses the diagnostic performance of deep learning methods for tumour detection in breast microwave sensing (BMS). <i>Methods:</i> A convolutional neural network (CNN) was used to predict the presence of a cancerous lesion in data from experimental scans of MRI-derived phantoms. An experimental dataset containing data from 1257 scans was used. The CNN was compared to a similarly sized dense neural network (DNN) and logistic regression classifier. <i>Results:</i> The CNN was able to exploit the sinogram data structure to achieve diagnostic performance significantly better than random classification, while neither the DNN nor logistic regression classifiers could generalize to unseen test data. The area under the curve of the receiver operating characteristic curve of the CNN classifier was estimated to be between (78 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 3)&#x0025; and (90 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 3)&#x0025;, where the upper estimate was obtained when the testing set was constrained to consist of phantoms with breast volumes that are within the volume bounds of the training set and when the tumour was located at the same vertical position as the system antennas. <i>Conclusion:</i> The results obtained in this investigation demonstrate the potential of combining deep learning and BMS systems for breast cancer detection. <i>Impact:</i> This paper provides an estimate of the diagnostic performance of an air-based BMS system using deep learning methods for automatic tumour detection on a sizable experimental dataset. The performance was found to be comparable to that of AI-assisted mammography and marks a first step toward larger-scale investigations in BMS.

A novel focused active microwave system is investigated for detecting tumors in the breast. In contrast to X-ray and ultrasound modalities, the method reviewed here exploits the breast-tissue physical properties unique to the microwave spectrum, namely, the translucent nature of normal breast tissues and the high dielectric contrast between malignant tumors and surrounding lesion-free normal breast tissues. The system uses a pulsed confocal technique and time-gating to enhance the detection of tumors while suppressing the effects of tissue heterogeneity and absorption. Using published data for the dielectric properties of normal breast tissues and malignant tumors, we have conducted a two-dimensional (2-D) finite-difference time-domain (FDTD) computational electromagnetics analysis of the system. The FDTD simulations showed that tumors as small as 2 mm in diameter could be robustly detected in the presence of the background clutter generated by the heterogeneity of the surrounding normal tissue. Lateral spatial resolution of the tumor location was found to be about 0.5 cm.